Material Stock and Embodied Greenhouse Gas Emissions of Global and Urban Road Pavement

Roads play a key role in movements of goods and people but require large amounts of materials emitting greenhouse gases to be produced. This study assesses the global road material stock and the emissions associated with materials’ production. Our bottom-up approach combines georeferenced paved road segments with road length statistics and archetypical geometric characteristics of roads. We estimate road material stock to be of 254 Gt. If we were to build these roads anew, raw material production would emit 8.4 GtCO2-eq. Per capita stocks range from 0.2 t/cap in Chad to 283 t/cap in Iceland, with a median of 20.6 t/cap. If the average per capita stock in Africa was to reach the current European level, 166 Gt of road materials, equivalent to the road material stock in North America and in East and South Asia, would be consumed. At the urban scale, road material stock increases with the urban area, population density, and GDP per capita, emphasizing the need for containing urban expansion. Our study highlights the challenges in estimating road material stock and serves as a basis for further research into infrastructure resource management.

. 75 Table S1 -Aggregation of Köppen-Geiger climate zones into four climate classes based on the LTPP classification 76 S1.2. Length of the global road network 77 The polylines representing the global road network in the GRIP dataset are projected to calculate their 78 length in meters. The projection chosen for each GRIP region is given in Table S2. 79   85 The road archetypes developed in this study are typical representation of roads according to the 86 country, the climate class ((1) wet, non-freeze, (2) dry, non-freeze, (3) wet, freeze, and (4) dry, 87 freeze), the road type (the five road types from GRIP: (1) highway, (2) primary, (3), secondary, (4) 88 tertiary, and (5) local), and the pavement type (asphalt or concrete). Road type is considered to some 89 extent a function of traffic volume. Our archetypes consist only of their material composition, energy 90 used for their construction is out of the scope. 91

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The typical number of lanes for each road segment is collected from OpenStreetMap (OSM) using the 93 Python library Pyrosm 9 . OSM is an open-source geographic database built on a collaborative effort of 94 users across the globe and is an easy solution to get geometric characteristics of roads in a large panel 95 of countries. The attribute "number of lanes" is chosen over the attribute "width" as more road 96 segments in OSM have this attribute and would constitute a better sample for estimating width of road 97 in a later stage. Dual carriageways in OSM are mapped as two parallel lines while in GRIP, they are 98 mapped as one line to avoid double counting of the road length. For road types "motorway", 99 "primary", "trunk" and "secondary" in OSM, their number of lanes is multiplied by 2 if they are 100 tagged as being "oneway". It is assumed that "motorway", "primary", "trunk" and "secondary" roads 101 which are "oneway" are part of a dual carriageway. Road types from OSM are then mapped to the 102 road type from GRIP as shown in Table S3 obtained from Meijer et al. 1 . Lastly, weighted averages of 103 number of lanes by country and road type are calculated. 104

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Archetypes represent the pavement layers and thicknesses at age = 0 (the initial construction of the 115 road). During its lifetime, each road segment undergoes maintenance treatment such as overlays or 116 reconstruction. Therefore, the cross sections used to represent typical roads may not correspond to the 117 reality of the current road stock. However, this still enables us to provide an estimate of the global 118 road material stock. 119 Two pavement types are considered: flexible pavement and rigid pavement. The pavement consists 120 generally of three layers: subbase course, base course, and surface course. On one hand, the flexible 121 pavement has the surface course (and possibly the base course) made of asphalt * . On the other hand, 122 rigid pavement is made of concrete. The base and subbase courses are usually composed of granular 123 materials alone or with binding substances. There may be a large panel of configurations in the 124 composition of the different pavement layers. For the sake of consistency and simplicity in collecting 125 and organizing the data, it is assumed that the pavement consists of two layers: a layer of asphalt 126 (flexible pavement) or a layer of concrete (rigid pavement) on top of a layer of granular materials 127 (which can be bound with cement in some countries). 128 Due external constraints (as language barrier or ease of access), it was not possible to collect cross 129 section data for all the countries available in the GRIP dataset. Therefore, the data collection was 130 limited to a few countries in each GRIP region and derivations of archetypes for these countries were 131 used as proxies for the other countries in the same region. The countries for which data were collected 132 are presented in Table S6. If possible, data were collected to fit the road types from GRIP (highway, 133 primary, secondary, tertiary, and local roads) and the four climate classes (WN

S9
The layer thicknesses are in a next step converted to material intensities (in kg/m 2 ) using their 144 respective density collected from the database available in the software Athena Pavement LCA 48 as 145 averages of the products available in their database -2.3x10 3 kg/m 3 for asphalt, 2.4x10 3 kg /m 3 for 146 granular, 2.3x10 3 kg /m 3 for concrete and 3.15x10 3 kg/m 3 for cement. If the granular materials are 147 bound with cement, the ratio of cement is assumed to be 3% 49 by volume (ratio of cement from South 148 Africa 49 but assumed to be applicable to other regions). 149 In the GRIP dataset, the surface type does not specify if the road is paved with asphalt or concrete. 150 Therefore, the material intensities of flexible pavement and rigid pavement were combined into single 151 material intensities by applying ratios of flexible versus rigid pavements in each country (and by road 152 type if available).

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Asphalt is the mixture of bitumen (asphalt binder), aggregates and potentially additional substances 162 (such as fly ash or hydrated lime 55 ). The GHG emissions are accounted from "well-to-construction 163 site" i.e., from the production of raw materials to produce asphalt until the transportation to the 164 construction site. Table S8 provides details on the processes included in the modeling of asphalt 165 production. 166 Table S8 -Details on modeling of asphalt production 167

S3.1.1. Bitumen production 168
The bitumen production is divided into three stages: (1) crude oil extraction, (2) crude oil trade and 169 consumption, and (3) bitumen production. Data collection and processing are described in the 170 following sections. 171

S3.1.1.1. Crude oil extraction 172
Crude oil is a natural nonrenewable resource composed of hydrocarbons. It is extracted to be refined 173 into petroleum products such as gasoline, kerosene, or diesel fuel. The carbon intensity of crude oil 174 presents large variations depending on the crude oil density as well as extraction and processing 175 methods. 57 176

Phase of asphalt production Processes Inputs
Materials production Bitumen production Crude oil (see Sections S3.

S12
Crude oil extraction is responsible for most of the greenhouse gases (GHGs) emitted by bitumen 177 production: its contribution ranges from 50% to 63% according to the Life Cycle Assessment 178 performed for the Asphalt Institute 58 and is around 70% of the bitumen global warming potential 179 estimated by Eurobitume 59 . The importance of building a model that would estimate the carbon 180 intensity of crude oil consumed for bitumen production is thus emphasized. 181 Masnadi et al. 57 evaluates the carbon intensity of crude oil from well-to-refinery gate (exploration, 182 drilling, extraction, processing, and transport to the refinery). This study provides the average, the 5 th 183 and the 95 th percentiles of crude oil carbon intensity (gCO2-eq/MJ crude oil) for 90 countries. About 184 98% of crude oil produced in 2015 is covered. Only one year is available, but it is assumed that this 185 year is representative of crude oil extraction activities even if external factors might disrupt oil 186 supply 60 and influence the production. 187 Instead of using the average value of crude oil carbon intensity, we chose to reproduce its probability 188 density function. Most of the inputs of crude oil production model developed by Masnadi et al. 57 189 follow a lognormal distribution and lead to skewed 5 th and 95 th percentiles. It is thus assumed that 190 lognormal would be an appropriate distribution. A least squares optimization procedure is applied to 191 find, for each producing country, a lognormal distribution that would fit best the average, 5 th and 95 th 192 percentiles. Five countries (Indonesia, Kyrgyzstan, Bulgaria, Romania, Spain) have a relative error of 193 their fitted average of more than 20% (up to 59% for Indonesia). Indonesia, Kyrgyzstan, Bulgaria, and 194 Romania have a relative error of their fitted 5 th percentile of more than 20%. No country has a relative 195 error of their fitted 95 th percentile of more than 20%. The fitted average, 5 th and 95 th percentiles for the 196 other countries are considered reasonable. 197

S3.1.1.2. Crude oil trade and consumption 198
It is extremely difficult to determine how much crude oil extracted by a country is processed, 199 exported, and further transformed (e.g., into bitumen) in another country to be used by this country or 200 to be exported in another one. Crude oil can be imported and re-exported without any transformation; 201 it can be imported, processed, and used/exported; refined products can also be directly imported. 61 The BACI database has limitations (erroneous report of trade information, aggregation of several 209 products, trading partners and quantities might not be fully reported due to confidentiality, etc.) 65 but 210 it is to our knowledge the most comprehensive publicly available source of data to identify trade of 211 crude oil adapted to our modeling. Crude oil refining is a complex process for which carbon 212 intensities by country and crude oil type have been calculated 66 without identifying carbon intensity of 213 crude oil refining specifically for bitumen production. 214 Here we decide to build a simple model of crude oil trade and consumption relying on a few strong 215 assumptions which have their limits but are necessary in the light of data availability. The 216 assumptions are described throughout the model along with their respective equations. The model's 217 variables and their description are presented in Table S9. 218 219 Equation (1) shows the situation in which production is larger than exports. It is assumed countries 220 export their own production and consume what is left from their own production. In addition, it is 221 assumed that no exports are originally from imports. 222

Variables Description
Production of crude oil Exports of crude oil exp Exports of crude oil from imports (only stored in the country) Imports of crude oil Consumption total of crude oil by a country Consumption of crude oil from own production Consumption of crude oil resulting from imports S14 ≥ : Equation (1) This assumption would not consider countries which have a larger production than their exports but 223 would still import and later re-export. 224 If production is strictly smaller than exports (either production is null -seven countries in this 225 situation, or it is not null but smaller than exports -only three countries in this situation), it is 226 assumed the consumption from domestic production is null and we can calculate the export of crude 227 oil from imports as shown in Equation (2). 228 The consumption from imports can therefore be calculated according to Equation (3). 229 The total consumption of crude oil is thus the sum of consumption from domestic production and 230 from imports (Equation (4)). 231 We test the results of our model to check if we have: 232 For the period 2010-2015, only one country (Democratic Republic of the Congo) seems to export 233 more than they produce while they do not have any import. This leads to negative consumption of 234 crude oil. This could be due to missing or erroneous data. We choose to drop the country from the 235 model. 236

S15
We calculate the ratios of crude oil consumption from own production to crude oil consumption from 237 imports in Equation (6) Results of our model provide a disaggregation of crude oil consumption by country into crude oil 251 consumption resulting from own production of crude oil and from imports. Figure S1 presents the 252 ratio of crude oil consumption for a selected set of countries. 253 S16 254 Figure S1  Other inputs in bitumen production 267 These calculated GHG emissions per MJ of crude oil consumed are used to replace the GHG 268 emissions of petroleum in pitch production process (assumed to be representative of bitumen 269 production) from Ecoinvent v3.6 56 . The pitch production process is available for a few regions. One 270 pitch production process is associated with a GRIP region (which will be applied to the countries 271 located in the respective region) to estimate the GHG emissions from other activities in the production 272 of bitumen. 273 GHG emissions for bitumen production are thus estimated for all the countries we have been able to 278 calculate the ratios of crude oil consumption from own production and imports. Our model might 279 result in countries refining crude oil to produce bitumen while this might not be the case. However, it 280 is assumed that if countries are reported to produce and/or import crude oil, they have refining 281 facilities and would therefore produce bitumen. 282

S3.1.2. Aggregates production 283
The impact of aggregates is estimated based on crushed gravel activity from Ecoinvent v3.6 56 . The 284 crushed gravel markets available in the database consist of crushed gravel production and transport. 285 For each GRIP region, a crushed gravel market is selected and modified to be a generic representation 286 GRIP region Ecoinvent v3.6 process, pitch production North America pitch production, petroleum refinery operation, RoW Central and South America pitch production, petroleum refinery operation, BR Africa pitch production, petroleum refinery operation, ZA Europe pitch production, petroleum refinery operation, Europe without Switzerland Middle East and Central Asia pitch production, petroleum refinery operation, RoW South and East Asia pitch production, petroleum refinery operation, IN Oceania pitch production, petroleum refinery operation, RoW BR: Brazil; IN: India; RoW: Rest-Of-World; ZA: South Africa S18 of the activity in the region. When possible, the market was also adapted to fit one or more countries 287 located in the region. The modifications are made on the electricity used in the crushed gravel 288 production and on the transport specified in the market activity as specified in Table S11. The 289 transport distance is however unchanged. Calculations are performed with Arda (Allocation, cut-off 290 by classification and use of ReCiPe Midpoint (H) V1.13). 291 292 S19

S3.1.3. Asphalt mixture production 295
Asphalt mixture is composed of bitumen, natural aggregates, and potentially additives. Bitumen 296 represents about 4 to 6 % of the mixture weight 51 . Additives are ignored in our study. In addition, only 297 virgin materials are used. The quantity of bitumen is chosen between 4 and 6% according to a uniform 298 distribution and the quantity of crushed gravel is calculated based on the bitumen quantity so that the 299 sum is 100%. is natural gas which is powering the asphalt plant. In Austria, coal is also reported as powering the 310 analysed asphalt plant. 80 . Some of the studies also report the use of electricity in the asphalt 311 production (ranging from 0.3% 71 up to 10.9% 72 when reported). A complete overview on asphalt 312 plants would be required to define a typical energy mix by country or by region. However, this is 313 considered out of the scope of this study. Therefore, rough assumptions are made: the use of fossil 314 fuel ranges from 95% to 99% of the energy use (electricity completes the energy use) and fossil fuel 315 used is a mixed of fuel oil and natural gas (fuel oil ranging from 0 to 100% and natural gas 316 completing). The two parameters follow uniform distributions. 317 The GHG emissions of each energy type are taken from Ecoinvent v3.6 56 -ReCiPe Midpoint (H) 318 V1.13. The GHG emissions of energy are modeled by GRIP region. The processes considered are 319 listed in Table S12, Table S13, and Table S14. 320 S22

S3.1.4. Transportation to the construction site 324
The transport to the road construction site is reported ranging between 30 and 80 km 51 . A shorter 325 distance has also been reported 81 . Therefore, a range of 10-80 km is considered, being uniformly 326 distributed. The transport is assumed to be carried out by truck (even if in some countries, asphalt 327 might be transported by other transport means, e.g., in Norway, where it can be transported by boat 82 ). 328 The GHG emissions per tonne-km of asphalt transport are based on modified Ecoinvent v3.6 56 329 processes or other sources as described in Table S15. 330 GRIP region Ecoinvent v3.6 processes North America market group for electricity, medium voltage, RNA Central and South America market group for electricity, medium voltage, RLA Africa market group for electricity, medium voltage, RAF Europe market group for electricity, medium voltage, RER Middle East and Central Asia market group for electricity, medium voltage, RME South and East Asia market group for electricity, medium voltage, RAS Oceania market for electricity, medium voltage, AU AU: Australia; RAF: Africa; RAS: Asia and the Pacific; RER: Europe; RLA: Latin America and the Caribbean; RME: Middle East; RNA: North America

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The GHG emissions for granular materials are taken from aggregates described in Section S3.1.2. 345

346
Concrete is modeled using the "market for concrete, normal" as shown in Table S16. 347 Table S16 -Ecoinvent v3.6 processes to model concrete production 348 Regarding cement, used as a binding component of aggregates in some regions, its use is modelled by 349 the production of Portland cement as described in Table S17    S30 An inspection of the material quantity by material type (granular, asphalt, concrete, and cement) in 399 Figure  having lower GHG intensity for both asphalt ( Figure S2) and aggregates than the US and India (GHG 408 intensity for aggregates in China is about 10% lower than for the US and 60% lower than for India). 409

S4. Limitations of GRIP paved road length
We also observe significant differences in the materials' contribution to the GHG emissions in each 410 country. While for both the US and China, GHG emissions from asphalt production are about 5.6-5.8 411 times the ones from aggregates production, India exhibits a rather high GHG intensity in aggregates 412 production and GHGs from asphalt production are only 2.8 times the ones of aggregates. This high 413 value is due to road transport from the Ecoinvent v3.6 56 process "market for gravel, crushed, IN" used 414 to model GHG emissions from aggregates production. 415  Table 2 are presented in Figure S9. We observe 454 a tail of data points on the left part of the graphs for which the prediction is larger than the 455 observation. We introduce an indicator: the percentage of each urban area's total surface area covered 456 by paved road surface, calculated as the paved road surface area divided by the urban area. Urban 457 areas having less than 0.25% of their area covered by paved road surface are identified in red in 458

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Two hypotheses are raised to explain this observation: (1) the tail could come from the limitations of 465 the GRIP dataset leading to an underestimation of the material stock in many urban areas, (2) the 466 model is biased towards urban areas with higher material stocks and does not adequately capture other 467 indicators that might explain the lower than predicted values for urban areas in the left tail. Further 468 S7.2. Roads-to-Buildings ratios 482 We calculate Roads-to-Residential Buildings ratio (RtRB) and Roads-to-Buildings ratio (RtB) for a 483 few individual countries for which material stock in buildings was available in the literature using the 484 ODYM-RECC model 112 and the results from Deetman et al. 113 . 485